Short-Packet URLLCs for Multihop MIMO Full-Duplex Relay Networks: Analytical and Deep-Learning-Based Real-Time Evaluation

In this study, we investigate multihop multiple-input multiple-output (MIMO) full-duplex relay (FDR) networks with short-packet ultra-reliability and low-latency communications (uRLLCs), where transmit-antenna selection/maximum-ratio combining and transmit-antenna selection/selection combining are leveraged as diversity techniques. Under quasi-static Rayleigh-fading channels and the finite-blocklength theory, the end-to-end block-error rate (BLER) performance of the considered FDR is analyzed and compared with that of half-duplex relaying systems, from which the effective throughput (ETP), energy efficiency (EE), reliability, and latency are also investigated \textcolor{black}{to comprehensively capture the performance trend of the considered systems. To analyze the diversity order and} gain further insights into the system design, an asymptotic BLER analysis in the high signal-to-noise ratio regime is provided. However, the derived analytical expressions contain non-elementary functions, making them intricate for practical implementations, particularly in real-time configurations.

Therefore, we have introduced a deep multiple-output neural network with short execution time, low computational complexity, and highly accurate estimation to overcome this hurdle. This network can serve as an efficient tool to rapidly respond to the necessary system parameters, such as transmit power and blocklength, when the services request specific ETP, EE, reliability, and latency. To corroborate the correctness of the theoretical analysis, extensive simulation results are provided under varying impacts of system parameters, e.g., numbers of channel uses, information bits of data, relays, and antennas.